class_docstr: torchaudio.models.RNNT()
  
  Recurrent neural network transducer (RNN-T) model.
  
  Note:
      To build the model, please use one of the factory functions.
  
  Args:
      transcriber (torch.nn.Module): transcription network.
      predictor (torch.nn.Module): prediction network.
      joiner (torch.nn.Module): joint network.
      attndim (int): TCPGen attention dimension
      biasing (bool): If true, use biasing, otherwise use standard RNN-T
      deepbiasing (bool): If true, use deep biasing by extracting the biasing vector
      embdim (int): dimension of symbol embeddings
      jointdim (int): dimension of the joint network joint dimension
      charlist (list): The list of word piece tokens in the same order as the output layer
      encoutdim (int): dimension of the encoder output vectors
      dropout_tcpgen (float): dropout rate for TCPGen
      tcpsche (int): The epoch at which TCPGen starts to train
      DBaverage (bool): If true, instead of TCPGen, use DBRNNT for biasing
code: def __init__(
      self,
      transcriber: _Transcriber,
      predictor: _Predictor,
      joiner: _Joiner,
      attndim: int,
      biasing: bool,
      deepbiasing: bool,
      embdim: int,
      jointdim: int,
      charlist: List[str],
      encoutdim: int,
      dropout_tcpgen: float,
      tcpsche: int,
      DBaverage: bool,
  ) -> None:
      super().__init__(transcriber, predictor, joiner)
      self.attndim = attndim
      self.deepbiasing = deepbiasing
      self.jointdim = jointdim
      self.embdim = embdim
      self.encoutdim = encoutdim
      self.char_list = charlist or []
      self.blank_idx = self.char_list.index("<blank>")
      self.nchars = len(self.char_list)
      self.DBaverage = DBaverage
      self.biasing = biasing
      if self.biasing:
          if self.deepbiasing and self.DBaverage:
              # Deep biasing without TCPGen
              self.biasingemb = torch.nn.Linear(
                  self.nchars, self.attndim, bias=False
              )
          else:
              # TCPGen parameters
              self.ooKBemb = torch.nn.Embedding(1, self.embdim)
              self.Qproj_char = torch.nn.Linear(
                  self.embdim, self.attndim
              )
              self.Qproj_acoustic = torch.nn.Linear(
                  self.encoutdim, self.attndim
              )
              self.Kproj = torch.nn.Linear(self.embdim, self.attndim)
              self.pointer_gate = torch.nn.Linear(
                  self.attndim + self.jointdim, 1
              )
      self.dropout_tcpgen = torch.nn.Dropout(dropout_tcpgen)
      self.tcpsche = tcpsche
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import torch
  from torchaudio.models import Conformer, RNNT
  from torchaudio.models.rnnt import _Joiner, _Predictor, _TimeReduction, _Transcriber
label_desc: Use image node_id 1 to create a new RNNTBiasing object from inherited base classes: RNNT with example: obj = RNNTBiasing(transcriber, predictor, joiner, attndim, biasing, deepbiasing, embdim, jointdim, charlist, encoutdim, dropout_tcpgen, tcpsche, DBaverage)